An increasing number of data-intensive distributed applications are being developed to serve various needs, such as processing very large data sets that generally cannot be handled by a single computer. Instead, clusters of computers are employed to distribute various tasks, such as organizing and accessing the data and performing related operations with respect to the data. Various applications and frameworks have been developed to interact with such large data sets, including Hive, HBase, Hadoop, Spark, Amazon S3, and CloudStore, among others.
At the same time, virtualization techniques have gained popularity and are now commonplace in data centers and other computing environments in which it is useful to increase the efficiency with which computing resources are used. In a virtualized environment, one or more virtual nodes are instantiated on an underlying host computer and share the resources of the underlying computer. Accordingly, rather than implementing a single node per host computing system, multiple nodes may be deployed on a host to more efficiently use the processing resources of the computing system. These virtual nodes may include full operating system virtual machines, Linux containers, such as Docker containers, jails, or other similar types of virtual containment nodes. However, although virtual nodes may more efficiently use the resources of the underlying host computing systems, difficulties arise in scaling the virtual nodes and providing data from large storage repositories to the individual virtual nodes.